Overview

Description

These methods transform the features into a lower-dimensional space while retaining as much information as possible.

Linear methods assume that the data lies approximately on a linear subspace of the high-dimensional space. They are simpler and computationally less intensive but may not capture complex structures in the data.

Linear Methods are best for data that lie on or near a linear subspace. They are simpler and computationally efficient.